15 AI College Courses for High School Students
If you are a high school student interested in exploring the field of artificial intelligence, taking an AI college course can be a practical next step. AI courses allow you to develop practical tech skills, learn from experts and professionals at renowned institutions around the world, and gain in-depth knowledge of this growing field. Many of these courses are offered by universities and colleges, ensuring you get access to advanced AI coursework, faculty-led instruction, and college-level rigor. The experience can prepare you for future academics and help you test your interests before committing to a college degree.
How are AI courses different from other programs in high school?
Unlike traditional high school programs, AI college courses allow you to learn at your own pace on a flexible schedule. These courses typically follow a structured curriculum, with modules progressing from basic to advanced concepts, allowing you to progressively explore and revisit a range of AI topics. Depending on the course you choose, you may dive into machine learning essentials, predictive modeling, data-driven decision-making, or the creation of custom GPTs. The courses may also help you develop a range of AI tools, including fraud detection, model deployment, and skills such as regression analysis, applied machine learning, and more.
To make your search easier, here are 15 AI college courses for high school students.
If you’re looking for online AI programs, check out our blog here.
1. MIT’s Beaver Works Summer Institute (BWSI): Serious Games Development with Artificial Intelligence Course
Location: Virtual or in-person at MIT, Cambridge, MA
Cost: Free for students with a family income of less than $200,000; $2,400 otherwise
Acceptance rate/cohort size: Not specified
Dates: Online prerequisite courses: February 2 – June 19; Summer program: July 6 – August 2
Application deadline: March 30; application opens on March 2, and registration for online courses begins in December.
Eligibility: U.S. high school students in grades 9 – 11 who have completed the online prerequisite courses; check course-specific prerequisites here.
BWSI is a four-week summer program for high school students who are interested in exploring advanced tech and engineering through project-based coursework. The Serious Games Development with Artificial Intelligence course teaches you how to combine machine learning and game-based modeling to analyze real-world technology and policy questions. You will build a full game in Python focused on a zombie disease outbreak scenario, exploring how AI and human decision-making affect public health outcomes. In the process, you will learn about backend game development, AI ethics, systems modeling, user interface design, and data logging and analysis. You will also develop professional software skills by working in Agile development teams, managing issues, and collaborating on a shared codebase. By the end, you will have designed and coded your own game extension, run live data-collection experiments, and presented your results.
2. UW Youth & Teen Programs: Introduction to AI & Machine Learning
Location: Virtual or University of Washington, Seattle, WA
Cost: $895 + $50 registration fee each quarter
Acceptance rate/cohort size: Not specified
Dates: March 31 – May 28 | June 29 – July 10 | July 13 – 24 | July 27 – August 7 | August 10 – 21
Application deadline: Spring session: March 9 | Summer session: Two weeks before the session starts
Eligibility: High school students with foundational knowledge of Python, either through a course such as Coding in Python I or self-study, and familiarity with navigating and using code libraries
This course allows you to explore how artificial intelligence functions and its role in everyday technologies. You will dive into machine learning, neural networks, computer vision, reinforcement learning, and generative AI while engaging in guided activities that let you experiment with building or modifying basic AI tools. The ethical implications of AI use, including fairness, transparency, and responsible use, are integrated throughout the course. You will also examine current and emerging applications of AI across sectors. Upon completing the course, you will earn a digital badge that can be added to your profile.
3. Stanford University’s AI in Healthcare Specialization
Location: Virtual via Coursera
Cost: Free to enroll; paid certificate available
Acceptance rate/cohort size: Open enrollment
Dates: Self-paced; typical commitment of four weeks
Application deadline: Open enrollment
Eligibility: Open to all; no prior experience required
AI in Healthcare Specialization, offered by Stanford University, is a beginner-level four-week course focused on the link between AI and healthcare. During the course, you will learn about patient care safety, quality, and research, and identify the problems faced by healthcare providers and how machine learning can solve them. The modules also cover topics like clinical research, data ethics, machine learning, data mining, health informatics, applied machine learning, and model evaluation. You will also learn tools like Model Deployment and participate in a healthcare capstone project during this five-course specialization.
4. Syracuse University’s Build Your Future with Artificial Intelligence Pre-College Course
Location: Syracuse University, Syracuse, NY
Cost: $4,995 (residential) | $4,024 (commuter); discounts and scholarships available
Acceptance rate/cohort size: Not specified
Dates: July 19 – 31
Application deadline: May 1
Eligibility: Rising high school sophomores, juniors, seniors, and graduating high school seniors
This two-week, beginner-friendly course at Syracuse University introduces you to how AI works and how it's being used across fields like healthcare, sports, and climate science. You will get hands-on exposure to AI tools while creating projects, solving problems, and exploring AI’s real-world applications. The course covers how AI learns from data and helps you think critically about fairness and responsibility in technology. Classes are a mix of lectures, coding sessions, project work, and potential field trips to meet AI professionals. You will live on campus in a residence hall and participate in activities with other students outside of class hours. On completing the course, you will receive a Certificate of Completion, and you can request a Syracuse University noncredit transcript.
5. University of California, Davis: Big Data, Artificial Intelligence, and Ethics
Location: Virtual via Coursera
Cost: Free to enroll; paid certificate available
Acceptance rate/cohort size: Open enrollment
Dates: Self-paced; typical commitment of nine hours
Application deadline: Open enrollment
Eligibility: Open to all; no prior experience required
This beginner-level online course from UC Davis teaches you how big data and AI are reshaping society, and what ethical questions those changes raise. You will learn what big data is, how machine learning works, and how AI is used across fields like healthcare, education, politics, and business. The course includes hands-on labs in which you use IBM Watson to analyze personality using natural language processing and train your own machine learning models with Google's teachable machines. You will also explore how social media platforms use persuasive technology and how ethics applies to decisions made by AI systems. The course takes about nine hours to complete across three modules, and you can complete it at your own pace.
6. Virginia Tech’s Explore the Future: Generative AI for High School Innovators Course
Location: Online
Cost: $400 (Virginia in-state students) | $1,000 (out-of-state students); scholarships available for Virginia students through an essay contest
Acceptance rate/cohort size: Not specified
Dates: July 7 – 18
Application deadline: Not specified; essay contest deadline for scholarship: June 20
Eligibility: Students in grades 9 – 12; no prior coding or AI experience required
This two-week online summer course offered by Virginia Tech introduces you to the fundamentals of machine learning and generative AI across 10 sessions. You will learn how AI tools like ChatGPT, DALL-E, and Midjourney work, and get hands-on experience building simple machine learning models using platforms like Amazon SageMaker. The course covers core concepts including neural networks, natural language processing, data preprocessing, and the ethics of AI use. Each session runs two hours and includes a mix of instruction and hands-on activities, with a short break built in. The course wraps up with a capstone project where you will build and present your own AI-powered project, either individually or with a team. On completing the course, you will receive a certificate of completion from Virginia Tech.
7. Vanderbilt University’s Agentic AI and AI Agents for Leaders Specialization
Location: Virtual via Coursera
Cost: Free to enroll; paid certificate available
Acceptance rate/cohort size: Open enrollment
Dates: Self-paced; typical commitment of four weeks
Application deadline: Open enrollment
Eligibility: Open to all; no prior experience required
Agentic AI and AI Agents for Leaders Specialization is a four-week course focused on the intersection of machine learning and strategic decision-making in an organisational setting. You will dive into topics like AI product strategy, workflow management, generative AI agents, artificial intelligence, LLM application, and business process automation. You will also learn about prompt engineering, agentic workflows, and AI orchestration, and examine and implement safe and strategic automation with chatbots. The course also offers the opportunity to work on an applied learning project and earn a certificate from Vanderbilt University.
8. University of Illinois Urbana–Champaign’s Introduction to Artificial Intelligence
Location: Virtual via Coursera
Cost: Free to enroll; paid certificate available
Acceptance rate/cohort size: Open enrollment
Dates: Self-paced; typical commitment of two weeks
Application deadline: Open enrollment
Eligibility: Open to all; no prior experience required
Introduction to Artificial Intelligence by the University of Illinois Urbana-Champaign is a self-paced, beginner-level course that walks you through the history, core concepts, and real-world applications of AI across its four modules. You will learn how machine learning works, including classification, regression, and clustering, and move on to more advanced topics like deep learning, neural networks, and generative AI. The course also covers how AI is used in fields such as healthcare, marketing, and manufacturing, as well as the ethical, bias, and regulatory issues you should consider. The final module looks at where AI is headed, including the possibility of Artificial General Intelligence (AGI) and what that could mean for society and the workforce. The course also includes assignments and a peer-reviewed project to help you assess your progress.
9. Harvard University’s Machine Learning and AI with Python
Location: Virtual via edX
Cost: Free to enroll; certificate available for $299
Acceptance rate/cohort size: Open enrollment
Dates: Self-paced; typical commitment of six weeks
Application deadline: Open enrollment
Eligibility: Open to all; no prior experience required
This Harvard course teaches you how to use machine learning with Python to analyze data and make better decisions. You will start by exploring decision trees, i.e., the foundational algorithm of machine learning, and then move on to more advanced techniques like random forests and gradient boosting. Using real-world datasets, you will train models, test predictions, and learn how to spot problems like overfitting, data bias, and underfitting along the way. You will also gain hands-on experience with Python libraries used in machine learning, and the opportunity to build a foundation for further study in data science and AI.
10. The Georgia Institute of Technology: Foundations of Generative AI
Location: Virtual via edX
Cost: Free to enroll; certificate available for an additional cost
Acceptance rate/cohort size: Open enrollment
Dates: Self-paced; typical commitment of three weeks
Application deadline: Open enrollment
Eligibility: Open to all; no prior experience required
This Georgia Institute of Technology course is a three-week self-paced program focused on generative artificial intelligence. You will learn about the evolution of AI, including the statistical, classical, and generative paradigms and the limitations, strengths, and ethical considerations of generative AI. You will learn the foundational principles related to generative models, understand the large model training processes, and gain hands-on experience in the responsible use of AI tools. The course also covers concepts like context windows, probability distributions, tokens, and loss functions.
11. University of Michigan’s Innovations in Investment Technology: Artificial Intelligence
Location: Virtual via Coursera
Cost: Free to enroll; paid certificate available
Acceptance rate/cohort size: Open enrollment
Dates: Self-paced; typical commitment of one week
Application deadline: Open enrollment
Eligibility: Open to all; no prior experience required
University of Michigan’s Innovations in Investment Technology: Artificial Intelligence is a beginner-level course that explores how AI and technology are changing the way people invest and manage money. You will learn how robo-advisors work, how to build a diversified portfolio, and how to evaluate the strengths and weaknesses of both human and AI-driven financial advisors. The course also covers stock selection strategies, focusing on topics like fundamental analysis, neural networks, and smart beta investing. The final module examines how big data and machine learning are being applied in investment management today, including the opportunities and pitfalls associated with them. Coursework includes assignments, a mix of video lectures, and case study readings.
12. University of Illinois Urbana–Champaign’s Artificial Intelligence in Wealth Management
Location: Virtual via Coursera
Cost: Free to enroll; paid certificate available
Acceptance rate/cohort size: Open enrollment
Dates: Self-paced; typical commitment of nine hours
Application deadline: Open enrollment
Eligibility: Open to all; no prior experience required
This course from the University of Illinois Urbana-Champaign teaches you how AI is changing the work of financial advisors and wealth managers across three modules totaling about nine hours. The first module covers what AI actually is, how it differs from basic statistical tools, and what compliance and ethics guardrails apply when using it in financial services. The second module introduces advanced AI tools for retirement planning and financial advisory, featuring conversations with fintech founders, senior financial planners, and other industry professionals. The final module examines the future of AI in wealth management, including how to balance automation with the human side of financial advising and navigate legal and ethical risks. The course is non-credit and part of the broader Artificial Intelligence in Finance and Wealth Management specialization.
13. University of Pennsylvania: AI For Business Specialization
Location: Virtual via Coursera
Cost: Free to enroll; paid certificate available
Acceptance rate/cohort size: Open enrollment
Dates: Self-paced; typical commitment of four weeks
Application deadline: Open enrollment
Eligibility: Open to all; no prior experience required
The AI for Business Specialization offered by the University of Pennsylvania is a four-week introductory course for beginners interested in the intersection of business and AI. The specialization is a series of four distinct yet related courses in which you will learn from industry experts, develop an in-depth understanding of key AI concepts, and have the opportunity to earn a shareable certificate from the University of Pennsylvania. You will learn how artificial intelligence, machine learning, and big data can be used in different areas of a business. You will learn about tech-enabled marketing strategies, the role of personalization in enhancing the customer journey, analytics, responsible AI, data-driven decision making, human resources, and machine learning. You will also have the opportunity to learn about tools like Generative AI, Fraud Detection, and Generative Adversarial Networks (GANs).
14. University of Colorado Boulder’s Mind and Machine Specialization
Location: Virtual via Coursera
Cost: Free to enroll; paid certificate available
Acceptance rate/cohort size: Open enrollment
Dates: Self-paced; typical commitment of four weeks
Application deadline: Open enrollment
Eligibility: Open to all; no prior experience required
This is a four-course specialization offered by the University of Colorado Boulder that explores the relationship between human thinking and artificial intelligence through the lens of cognitive science. You will start with big foundational questions, such as what the mind is, what AI is, and how we even test for intelligence, and then move into how humans and machines approach problem-solving, decision-making, and bias. The third course focuses on how both humans and computers process visual information, including neural networks and computer vision. The final course examines how social, developmental, and evolutionary factors shape the mind and introduces concepts such as game theory and autonomous agents.
15. University of Michigan’s Understanding Data: Navigating Statistics, Science, and AI Specialization
Location: Virtual via Coursera
Cost: Free to enroll; paid certificate available
Acceptance rate/cohort size: Open enrollment
Dates: Self-paced; typical commitment of three months
Application deadline: Open enrollment
Eligibility: Open to all; no prior experience required
This three-course specialization from the University of Michigan teaches you how to think critically about data, statistics, and AI. The first course covers how data is collected, summarized, and visualized, and how to judge whether a statistic is reliable. The second course digs into how scientific research works, including how to interpret p-values, spot bias in study design, and evaluate whether news headlines accurately represent research findings. The third course focuses on AI, walking you through key concepts such as machine learning and generative AI, explaining how AI systems are built and evaluated, and helping you recognize when AI claims are misleading or overstated. Across all three courses, you will work on hands-on projects applying these skills to real-world scenarios.
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